“Bizindan aabajitoon Shkiizhiigoon gaye gitowagan” Listen using Eyes and Ears
Bibliographic record
Abstract
Boozhoo, Amanda Shawayahamish ni-di-shin-i-hkaaz, Animbiigoo Zaagi'igan Anishinaabek ni-n-doo-jii. Hi, my name is Amanda Shawayahamish. I come from Lake Nipigon Reserve. I am an Anishinaabe (Ojibwe) woman, wife, stepmother, Auntie, bead artist and graduate student at Concordia University exploring reconnection and reclamation to my Anishinaabe identity and traditions through beadwork, storytelling and strengthening relationships with my family and community. I blend autoethnographic and Indigenous Research Methodology to tell the story of my family and myself on this journey. Auto-ethnography in an Anishinaabe context will be a guide to tell my “story” in a narrative research-creation project. I share my experience and journal entries from my and my family’s visit to Ombabika and Auden, Ontario where my ancestors originated. My research collaborators are my family and cherished members of my community, so it is very personal. Sometimes, it feels as if the expectations of the university are counterchallenging what I know, and our ways of being as Anishinaabe. My process is not linear; it’s a circular path. I hope that my experiences will guide other Indigenous Peoples on their pathway. The objective of my MA research-creation project is to challenge academic conventions and demonstrate the importance of Indigenous Research Methodologies and approaches. \nKeywords: Indigenous, Anishinaabe women, Anishinaabe, Indigenous autoethnography, Indigenous storytelling, Anishinaabe storytelling, Reclaiming Anishinaabe identity, reclamation, Anishinaabe and/or Indigenous beadwork
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".